
'''simple docstring'''

import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import DecisionTransformerModel
   from transformers.models.decision_transformer.modeling_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )







class       __lowerCAmelCase       :



     def __init__(self		,      lowerCAmelCase__		,      lowerCAmelCase__=1_3		,      lowerCAmelCase__=7		,      lowerCAmelCase__=6		,      lowerCAmelCase__=1_7		,      lowerCAmelCase__=2_3		,      lowerCAmelCase__=1_1		,      lowerCAmelCase__=True		,      ):
          _UpperCAmelCase   :    Tuple			    =       parent
          _UpperCAmelCase   :    Optional[int]			    =       batch_size
          _UpperCAmelCase   :    Optional[Any]			    =       seq_length
          _UpperCAmelCase   :    str			    =       act_dim
          _UpperCAmelCase   :    Union[str, Any]			    =       state_dim
          _UpperCAmelCase   :    Dict			    =       hidden_size
          _UpperCAmelCase   :    Tuple			    =       max_length
          _UpperCAmelCase   :    Tuple			    =       is_training



     def        snake_case_					(self      ):
          _UpperCAmelCase   :    Tuple			    =       floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
          _UpperCAmelCase   :    Dict			    =       floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
          _UpperCAmelCase   :    Union[str, Any]			    =       floats_tensor((self.batch_size, self.seq_length, 1)      )
          _UpperCAmelCase   :    Union[str, Any]			    =       floats_tensor((self.batch_size, self.seq_length, 1)      )
          _UpperCAmelCase   :    int			    =       ids_tensor((self.batch_size, self.seq_length)		,      vocab_size=1_0_0_0      )
          _UpperCAmelCase   :    Any			    =       random_attention_mask((self.batch_size, self.seq_length)      )

          _UpperCAmelCase   :    Any			    =       self.get_config()

          return (
              config,
              states,
              actions,
              rewards,
              returns_to_go,
              timesteps,
              attention_mask,
          )



     def        snake_case_					(self      ):
          return DecisionTransformerConfig(
              batch_size=self.batch_size		,      seq_length=self.seq_length		,      act_dim=self.act_dim		,      state_dim=self.state_dim		,      hidden_size=self.hidden_size		,      max_length=self.max_length		,      )



     def        snake_case_					(self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      ):
          _UpperCAmelCase   :    str			    =       DecisionTransformerModel(config=__UpperCamelCase      )
          model.to(__UpperCamelCase      )
          model.eval()
          _UpperCAmelCase   :    List[str]			    =       model(__UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase      )

          self.parent.assertEqual(result.state_preds.shape		,      states.shape      )
          self.parent.assertEqual(result.action_preds.shape		,      actions.shape      )
          self.parent.assertEqual(result.return_preds.shape		,      returns_to_go.shape      )
          self.parent.assertEqual(
              result.last_hidden_state.shape		,      (self.batch_size, self.seq_length * 3, self.hidden_size)      )  # seq length *3 as there are 3 modelities: states, returns and actions



     def        snake_case_					(self      ):
          _UpperCAmelCase   :    Union[str, Any]			    =       self.prepare_config_and_inputs()
          (
              (
              _UpperCAmelCase
          )    ,	(
              _UpperCAmelCase
          )    ,	(
              _UpperCAmelCase
          )    ,	(
              _UpperCAmelCase
          )    ,	(
              _UpperCAmelCase
          )    ,	(
              _UpperCAmelCase
          )    ,	(
              _UpperCAmelCase
          )    ,	
          )   :    Tuple			    =       config_and_inputs
          _UpperCAmelCase   :    Optional[int]			    =       {
              """states""": states,
              """actions""": actions,
              """rewards""": rewards,
              """returns_to_go""": returns_to_go,
              """timesteps""": timesteps,
              """attention_mask""": attention_mask,
          }
          return config, inputs_dict







@require_torch
class       __lowerCAmelCase       (    _UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase							):
     snake_case  :       str				 = (DecisionTransformerModel,) if is_torch_available() else ()
     snake_case  :       Any				 = ()
     snake_case  :       Optional[int]				 = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

     # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
     snake_case  :       str				 = False

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     snake_case  :       str				 = False
     snake_case  :       Union[str, Any]				 = False
     snake_case  :       Optional[int]				 = False
     snake_case  :       Union[str, Any]				 = False
     snake_case  :       Dict				 = False
     snake_case  :       List[Any]				 = False
     snake_case  :       str				 = False
     snake_case  :       str				 = False
     snake_case  :       str				 = False



     def        snake_case_					(self      ):
          _UpperCAmelCase   :    List[str]			    =       DecisionTransformerModelTester(self      )
          _UpperCAmelCase   :    Optional[int]			    =       ConfigTester(self		,      config_class=__UpperCamelCase		,      hidden_size=3_7      )



     def        snake_case_					(self      ):
          self.config_tester.run_common_tests()



     def        snake_case_					(self      ):
          _UpperCAmelCase   :    Optional[int]			    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase      )



     @slow
     def        snake_case_					(self      ):






          for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase   :    Union[str, Any]			    =       DecisionTransformerModel.from_pretrained(__UpperCamelCase      )
               self.assertIsNotNone(__UpperCamelCase      )



     def        snake_case_					(self      ):
          _UpperCAmelCase    ,	_UpperCAmelCase   :    str			    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _UpperCAmelCase   :    Optional[Any]			    =       model_class(__UpperCamelCase      )
               _UpperCAmelCase   :    Dict			    =       inspect.signature(model.forward      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase   :    Any			    =       [*signature.parameters.keys()]

               _UpperCAmelCase   :    Tuple			    =       [
                   """states""",
                   """actions""",
                   """rewards""",
                   """returns_to_go""",
                   """timesteps""",
                   """attention_mask""",
               ]

               self.assertListEqual(arg_names[: len(__UpperCamelCase      )]		,      __UpperCamelCase      )







@require_torch
class       __lowerCAmelCase       (    unittest.TestCase							):



     @slow
     def        snake_case_					(self      ):
          _UpperCAmelCase   :    Optional[int]			    =       2  # number of steps of autoregressive prediction we will perform
          _UpperCAmelCase   :    Dict			    =       1_0  # defined by the RL environment, may be normalized
          _UpperCAmelCase   :    Dict			    =       DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""      )
          _UpperCAmelCase   :    Optional[Any]			    =       model.to(__UpperCamelCase      )
          _UpperCAmelCase   :    List[Any]			    =       model.config
          torch.manual_seed(0      )
          _UpperCAmelCase   :    Any			    =       torch.randn(1		,      1		,      config.state_dim      ).to(device=__UpperCamelCase		,      dtype=torch.floataa      )  # env.reset()

          _UpperCAmelCase   :    Optional[int]			    =       torch.tensor(
              [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]		,      device=__UpperCamelCase      )

          _UpperCAmelCase   :    List[Any]			    =       torch.tensor(__UpperCamelCase		,      device=__UpperCamelCase		,      dtype=torch.floataa      ).reshape(1		,      1		,      1      )
          _UpperCAmelCase   :    Dict			    =       state
          _UpperCAmelCase   :    Union[str, Any]			    =       torch.zeros(1		,      0		,      config.act_dim		,      device=__UpperCamelCase		,      dtype=torch.floataa      )
          _UpperCAmelCase   :    Dict			    =       torch.zeros(1		,      0		,      device=__UpperCamelCase		,      dtype=torch.floataa      )
          _UpperCAmelCase   :    str			    =       torch.tensor(0		,      device=__UpperCamelCase		,      dtype=torch.long      ).reshape(1		,      1      )

          for step in range(__UpperCamelCase      ):
               _UpperCAmelCase   :    Optional[Any]			    =       torch.cat([actions, torch.zeros(1		,      1		,      config.act_dim		,      device=__UpperCamelCase      )]		,      dim=1      )
               _UpperCAmelCase   :    str			    =       torch.cat([rewards, torch.zeros(1		,      1		,      device=__UpperCamelCase      )]		,      dim=1      )

               _UpperCAmelCase   :    Union[str, Any]			    =       torch.ones(1		,      states.shape[1]      ).to(dtype=torch.long		,      device=states.device      )

               with torch.no_grad():
                    _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase   :    List[Any]			    =       model(
                        states=__UpperCamelCase		,      actions=__UpperCamelCase		,      rewards=__UpperCamelCase		,      returns_to_go=__UpperCamelCase		,      timesteps=__UpperCamelCase		,      attention_mask=__UpperCamelCase		,      return_dict=__UpperCamelCase		,      )

               self.assertEqual(action_pred.shape		,      actions.shape      )
               self.assertTrue(torch.allclose(action_pred[0, -1]		,      expected_outputs[step]		,      atol=1e-4      )      )
               _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase   :    List[Any]			    =       (  # env.step(action)
                   torch.randn(1		,      1		,      config.state_dim      ).to(device=__UpperCamelCase		,      dtype=torch.floataa      ),
                   1.0,
                   False,
                   {},
               )

               _UpperCAmelCase   :    Union[str, Any]			    =       action_pred[0, -1]
               _UpperCAmelCase   :    Tuple			    =       torch.cat([states, state]		,      dim=1      )
               _UpperCAmelCase   :    Union[str, Any]			    =       returns_to_go[0, -1] - reward
               _UpperCAmelCase   :    List[str]			    =       torch.cat([returns_to_go, pred_return.reshape(1		,      1		,      1      )]		,      dim=1      )
               _UpperCAmelCase   :    Any			    =       torch.cat(
                   [timesteps, torch.ones((1, 1)		,      device=__UpperCamelCase		,      dtype=torch.long      ) * (step + 1)]		,      dim=1      )




"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)